Neuro-Symbolic Intent-Based Intrusion Detection System for Internet of Medical Things
Bibliographic record
Abstract
The Internet of Medical Things (IoMT) introduces complex security challenges as interconnected medical devices enlarge the attack surface and limit the effectiveness of traditional intrusion detection systems (IDS). In this paper, we propose a Neuro-Symbolic Intent-Based Intrusion Detection System (NS-IBN) that integrates deep learning–based pattern recognition with symbolic reasoning to produce interpretable, intent-aligned security decisions. NS-IBN comprises an Intent-to-Symbol Translation Layer, an Intent-Driven Attention Mechanism, a Neural-Symbolic Synchronization Module, and a Symbolic Reasoning Engine that together link administrator-defined security intents to concrete detection behavior. In a representative intensive care unit (ICU) scenario with networked infusion pumps and vital-sign monitors, NS-IBN can be configured to detect lateral movement and unauthorized command injection while limiting disruptive false alarms for clinicians. Evaluation on the IoT-IDS2021 benchmark shows that NS-IBN achieves 98.3% accuracy, an explainability score of 0.94, and a 1.2% false positive rate, providing transparent and auditable intrusion detection for IoMT environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".